Application of Variational Quantum Algorithms to Autonomous Ground Vehicle Mobility

Abstract

We examine one VQA in particular called the Quantum Approximate Optimization Algorithm (QAOA). We show how to map the problem of clustering a dataset onto a Max-Cut problem, and give an outline of how to solve Max-Cut using QAOA. We also introduce a method for improving the accuracy of QAOA by using the solution to a Max-Cut relaxation to warm-start the initial quantum state. We summarize several existing warm-starting approaches and compare their performance in simulated runs of QAOA. We also present some results for warm-started QAOA runs on existing quantum hardware.

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Document Details

Document Type
Technical Report
Publication Date
Dec 19, 2023
Accession Number
AD1218362

Entities

People

  • David Gorsich
  • James Stokes
  • Jeremy Mange
  • Paramsothy Jayakumar
  • Sam Cochran
  • Shravan Veerapaneni

Organizations

  • University of Michigan

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Automata Theory
  • Autonomous Navigation
  • Autonomous Systems
  • Autonomous Vehicles
  • Collision Avoidance
  • Computer Programming
  • Computer Vision
  • Computers
  • Ground Vehicles
  • Motion Planning
  • Probability
  • Quantum Algorithms
  • Quantum Bits
  • Quantum Circuits
  • Quantum Computing
  • Unmanned Ground Vehicles

Fields of Study

  • Computer science

Readers

  • Operations Research

Technology Areas

  • Quantum Computing